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--- |
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language: en |
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tags: |
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- tapas |
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license: apache-2.0 |
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datasets: |
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- wikisql |
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--- |
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# TAPAS large model fine-tuned on WikiSQL (in a supervised fashion) |
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his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). |
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This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), and [WikiSQL](https://github.com/salesforce/WikiSQL). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). |
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The other (non-default) version which can be used is: |
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- `no_reset`, which corresponds to `tapas_wikisql_sqa_inter_masklm_large` (intermediate pre-training, absolute position embeddings). |
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Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by |
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the Hugging Face team and contributors. |
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## Model description |
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TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. |
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This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it |
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can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it |
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was pretrained with two objectives: |
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- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in |
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the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. |
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This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, |
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or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional |
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representation of a table and associated text. |
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- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating |
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a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence |
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is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements. |
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This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used |
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to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed |
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or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQA and WikiSQL. |
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## Intended uses & limitations |
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You can use this model for answering questions related to a table. |
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For code examples, we refer to the documentation of TAPAS on the HuggingFace website. |
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## Training procedure |
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### Preprocessing |
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The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are |
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then of the form: |
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``` |
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[CLS] Question [SEP] Flattened table [SEP] |
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``` |
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The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts. |
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### Fine-tuning |
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The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512. |
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In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup |
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ratio of 0.1424. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12). |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{herzig2020tapas, |
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title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, |
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author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, |
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year={2020}, |
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eprint={2004.02349}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR} |
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} |
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``` |
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```bibtex |
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@misc{eisenschlos2020understanding, |
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title={Understanding tables with intermediate pre-training}, |
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author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller}, |
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year={2020}, |
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eprint={2010.00571}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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```bibtex |
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@article{DBLP:journals/corr/abs-1709-00103, |
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author = {Victor Zhong and |
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Caiming Xiong and |
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Richard Socher}, |
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title = {Seq2SQL: Generating Structured Queries from Natural Language using |
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Reinforcement Learning}, |
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journal = {CoRR}, |
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volume = {abs/1709.00103}, |
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year = {2017}, |
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url = {http://arxiv.org/abs/1709.00103}, |
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archivePrefix = {arXiv}, |
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eprint = {1709.00103}, |
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timestamp = {Mon, 13 Aug 2018 16:48:41 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1709-00103.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
